PROMPT-IML: Image Manipulation Localization with Pre-trained Foundation Models Through Prompt Tuning

• URL: http://arxiv.org/abs/2401.00653v1
• Date: Mon, 1 Jan 2024 03:45:07 GMT
• Title: PROMPT-IML: Image Manipulation Localization with Pre-trained Foundation Models Through Prompt Tuning
• Authors: Xuntao Liu, Yuzhou Yang, Qichao Ying, Zhenxing Qian, Xinpeng Zhang and Sheng Li
• Abstract summary: We present a novel Prompt-IML framework for detecting tampered images. Humans tend to discern authenticity of an image based on semantic and high-frequency information. Our model can achieve better performance on eight typical fake image datasets.
• Score: 35.39822183728463
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deceptive images can be shared in seconds with social networking services, posing substantial risks. Tampering traces, such as boundary artifacts and high-frequency information, have been significantly emphasized by massive networks in the Image Manipulation Localization (IML) field. However, they are prone to image post-processing operations, which limit the generalization and robustness of existing methods. We present a novel Prompt-IML framework. We observe that humans tend to discern the authenticity of an image based on both semantic and high-frequency information, inspired by which, the proposed framework leverages rich semantic knowledge from pre-trained visual foundation models to assist IML. We are the first to design a framework that utilizes visual foundation models specially for the IML task. Moreover, we design a Feature Alignment and Fusion module to align and fuse features of semantic features with high-frequency features, which aims at locating tampered regions from multiple perspectives. Experimental results demonstrate that our model can achieve better performance on eight typical fake image datasets and outstanding robustness.

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